Senior Machine Learning Engineer

About the Role

We are looking for a Senior Machine Learning Engineer to design, build, and optimize scalable ML models and systems. You will work closely with data scientists, software engineers, and product teams to deploy and maintain production-grade ML solutions.

Requirements

Key Responsibilities

• Design, develop, and deploy scalable and efficient ML models for real-world applications.

• Build and optimize end-to-end ML pipelines, including data preprocessing, feature engineering, model training, and inference.

• Improve model performance, reliability, and interpretability using best practices.

• Work with large-scale datasets and implement distributed ML systems using frameworks like TensorFlow, PyTorch, or JAX.

• Collaborate with data engineers to ensure efficient data pipelines and model serving.

• Optimize ML workflows using MLOps practices, including CI/CD for ML models and monitoring in production.

• Research and implement state-of-the-art ML techniques and stay up to date with industry trends.

• Mentor junior engineers and contribute to team growth through knowledge sharing.

Requirements

✅ 5+ years of experience in ML engineering, software development, or related fields.

✅ Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-Learn).

✅ Experience with ML model deployment in production using cloud platforms (AWS, GCP, Azure).

✅ Solid understanding of MLOps practices (CI/CD, monitoring, model versioning).

✅ Experience with distributed computing frameworks (Spark, Ray, Dask) and large-scale data processing.

✅ Strong understanding of software engineering best practices (version control, testing, clean code).

✅ Experience with containerization and orchestration tools (Docker, Kubernetes).

✅ Familiarity with deep learning, NLP, or recommendation systems is a plus.

✅ Strong problem-solving skills and ability to work in a fast-paced environment.

Nice-to-Have

➕ Experience with LLMs & Generative AI.

➕ Knowledge of graph ML, reinforcement learning, or federated learning.

➕ Contributions to open-source ML projects.

Benefits

🚀 Impact: Work on cutting-edge ML problems at scale.

🌍 Growth: Continuous learning, upskilling, and mentorship opportunities.

💻 Tech-First Culture: Work with modern ML stacks and tools.

🏡 Flexible Work: Remote-first with hybrid options available.